Research On Key Techniques Of Signal Reconstruction For Multifunctional Sensors

As one of the major trends of the modern sensor technology, multifunctionalsensing technology can satisfy the requirement of various monitoring systems in themeasurement accuracy, robustness and anti-jamming capability because of its smallsize, low power consumption, and the ability of overcome the cross-sensitivityeffect. At the same time, the input-output characteristics of multifunctional sensorsare more complex than traditional single-functional sensors, which require higherperformance of their signal reconstruction methods.The paper aims to study the key techniques of signal reconstruction formultifunctional sensors. First of all, a multifunctional sensor for onlinemeasurement of the two solute concentrations of ternary solution in the osmoticdehydration process is designed. Then, some key techniques for the signalreconstruction of multifunctional sensors based on this sensor, including the signalreconstruction method, the tradeoffs between the reconstruction accuracy andcomplexity, and the sample selection for multifunctional sensors are studied. Theresearch results of this subject can not only be used to achieve the measurement ofthe ternary solutions, but also be used in the signal reconstruction process for avariety of multifunctional sensors.The main study works are as follows:The multifunctional sensor for measuring the solute concentrations of ternarysolutions simultaneously is designed. The solute concentrations of ternary solutionsare achieved indirectly by measuring the temperature, the ultrasonic propagationvelocity and the conductivity of the solution. At the same time, to verify thefeasibility of the proposed methods, an experimental circuit based on themultifunctional sensor is designed to obtain the experimental data.The signal reconstruction of multifunctional sensors is studied and areconstruction method based on B-spline and Extended Kalman filter is presented.B-spline is used as the structure of the sensor inverse model and the modelparameters are estimated using the Extended Kalman filter. The results of thesimulations and experiments show that the proposed method can achieve highsignal reconstruction accuracy, and its reconstruction process is simple and suitableto be applied in the microprocessors which meet the requirement of online signalreconstruction for smart sensors.The method for the tradeoffs between the reconstruction accuracy andcomplexity in the signal reconstruction is studied. First, the impact of the B-splinestructure to the reconstruction accuracy and complexity is analyzed. Then the fittness function is designed and the genetic algorithm is adopted to optimize the B-spline model structure. The results of simulation and experiments show that theoptimized B-spline structure can reduce a lot of complexity without significantreduction of the signal reconstruction accuracy, and achieve the tradeoffs betweenthe reconstruction accuracy and complexity which can reduce the burden ofmicroprocessors.The sample selection method for the signal reconstruction of large batches ofmultifunction sensors is presented. Kernel subtracting clustering is adopted toclassify the high-dimension sample data of multifunctional sensors and select thetraining samples suitable for the signal reconstruction. The results of thesimulations show that although the number of training samples is reduced, thereconstruction method can still obtain high accuracy. Therefore, the presentedmethod can be used to reduce the workload of sampling in the signal reconstructionfor large batches of multifunctional sensors.